MONITORING AND ASSESSMENT OF LAND USE … · DOI: 2017, ALÖKI Kft., ... Kamrup (covering 4591.79 km² geographical area) were mapped using Landsat MSS,

Kumar: Monitoring and assessment of land use and land cover changes

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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 15(3):221-239.

http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)

DOI: http://dx.doi.org/10.15666/aeer/1503_221239

2017, ALÖKI Kft., Budapest, Hungary

MONITORING AND ASSESSMENT OF LAND USE AND LAND

COVER CHANGES (1977 - 2010) IN KAMRUP DISTRICT OF

ASSAM, INDIA USING REMOTE SENSING AND GIS

TECHNIQUES

KUMAR, D.

G.B. Pant National Institute of Himalayan Environment & Sustainable Development, Sikkim

Unit, Pangthang, Gangtok 737 101, Sikkim, India

e-mail: [email protected]

(Received 13th Oct 2016; accepted 3rd Mar 2017)

Abstract. Land use and land cover (LULC) has been changed significantly due to rapid urbanization at

alarming rate in the Kamrup district of Assam state of north-eastern region of India. The LULC of

Kamrup (covering 4591.79 km² geographical area) were mapped using Landsat MSS, TM and ETM+

remotely sensed images to focus on spatial and temporal changes in between 1977-2010. The LULC

maps with six major categories viz., dense forest, open forest, agriculture land, urban settlement, water

body and sand of the study site were generated using supervised classification approach. The result

interpreted that the initial dense forest cover in 1977 was approximately 29.08% (1335.42 km²) of the

total mapped area which has been decreased up to 23.83% (1094.16 km²) in 1987 and 22.34% (1025.81

km²) in 2010. Normalized Difference Vegetation Index (NDVI) of non-vegetative area is in increasing

similarly with urban settlement and open forest. The overall classification accuracy reached 76.34% in

1977, 89.56% in 1987 and 92.34 % in 2010. The extent of deforestation, change in LULC, expansion of

agriculture area, population pressure, forest clearance for agriculture practices has been studied using

remote-sensing coupled with field survey and the emphasis is given on the indictors of changes that has

been derived using temporal analysis.

Keywords: Landsat; LULC; NDVI; urbanization; forest cover

Introduction

Forests provide several ecologically, economically and socially perspective functions

to life viz., water supplies, soil conservation, nutrient cycling, species and genetic

diversity and green house gases regulation (Rao and Pand, 2001). Increasing

anthropogenic pressure such as land use/land cover changes, air, water, and soil

pollution (Fearnside, 2001; Sherbinin et al., 2007), degradation of soil quality and losses

in biological diversity causes the threatening of the overall productivity of ecosystem

functioning at regional as well as global scales (Noss, 2001; Kilic et al., 2004; Kumar,

2011). It is also concluded as vulnerability of places and people to climatic, economic

or sociopolitical perturbations (Kasperson et al., 1995; Turner et al., 2003; Lambin et

al., 2003). Agricultural practices have been the important factor for land transformation

in this world and nearly one third of the earth’s land surface is currently being used for

growing crops (FAO, 2004). Much of this agriculture land has been created at the

expense of natural forests, grassland and wetlands that provide valuable habitats for

species (MEA, 2003).

From last few years remote sensing has been widely used for several studies

including assessment of deforestation and forest cover changes (Hall et al., 1988;

Roughgarden et al., 1991; Wood and Skole, 1998; Kumar, 2011). Similarly, satellite

image classification, change analysis (Armenteras et al., 2006; Kumar, 2011) and

econometric modeling are also being used to identify the rates and drivers of

mailto:[email protected]


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deforestation in global hotspots of biodiversity and tropical ecosystems. Several studies

showed the utility of satellite remote sensing to monitoring the changes in LULC on the

basis of spatial and temporal remote sensed data (Wood and Skole, 1998; Lele and

Joshi, 2009; Malaviya et al., 2009). Fine resolutions with spatially explicit data on

landscape fragmentation are required to understand the impact of land use changes on

biological diversity (Liu et al., 2003). Satellites data have became a major application in

change detection because of the repetitive coverage of the satellites at short time

intervals (Mas, 1999). Using remote sensing, spatially explicit time series of

environmental data can be quickly obtained and updated (Dewan and Yamaguchi,

2009). In addition, GIS (Geographical Information System) technique provides the

software’s to spatial analysis, model and map environmental changes. Therefore, remote

sensing coupled with GIS recognized as a powerful and effective tool to monitor

environmental changes at broad scale especially in detecting the LULC change (Samant

and Subramanyam, 1998; Mas, 1999; Weng, 2002; Herold et al., 2003; Chauhan and

Nayak, 2005; Shamsudheen et al., 2005; Güler et al., 2007; Fan et al., 2007; Yu et al.,

2007; Boakye et al., 2008; Coskun et al., 2008; Hu et al., 2008; Granados-Ramirez et

al., 2008; Ardi and Wolff, 2009; Dewan and Yamaguchi, 2009; Malaviya et al., 2009;

Kamusoko and Aniya, 2009; Onur et al., 2009; Dong et al., 2010; Kumar, 2011).

Remotely sensed data for image analysis have been explored the various alterations

of the earth resources including forest cover and water bodies in common (Hashiba et

al., 2000; Giriraj et al., 2008). Remote sensing provides synoptic view of forest cover

and condition on real-time basis (Lillesand and Kiefer, 1999). Multi-temporal different

time scale data were used currently for the change detection of various landscapes

(Iverson et al., 1989; Lausia and Antonio, 2001). Therefore, this technique has attracted

the attentions of several investigators worldwide to use satellite multi-temporal different

time scale data in change detections of land cover (Chauhan and Nayak, 2005; Güler et

al., 2007; Granados-Ramirez et al., 2008; Ardi and Wolff, 2009; Dewan and

Yamaguchi, 2009; Malaviya et al., 2009; Onur et al., 2009; Dong et al., 2010). A field

survey combined with satellite remote sensing is useful which provides thematic maps

for vegetation types and floral/faunal distribution in certain define areas (Fuller et al.,

1998). Heterogeneous forest cover some time creates troubles to classify forest cover on

the basis of species composition (Boyd and Danson, 2005).

The forests are exploited for various purpose as timber, slash and burn cultivation

(shifting cultivation; jhum) and pasture development (De Moraes et al., 1998; Jha et al.,

2006; Giriraj et al., 2008), because of these anthropogenic activity natural LULC has

modified in to man-made LULC with poor species composition (Behera et al., 2005).

Deforestation has impacted on biogeochemical cycles and causes soil erosion, surface

runoff and water scarcity not only in the region, but also in the reasonably distant area

(Hill, 1999). Remotely sensed data are now available to map and monitor changes from

continental and local scales as well as over temporal scale (Rogan and Chen, 2004) such

as different period of Landsat satellite data are adequate for mapping land cover and

land use changes (Fuller et al., 1998; Srivastava et al., 2002; Fan et al., 2007; Merem

and Twumasi, 2007; Yu et al., 2007; Boakye et al., 2008; Coskun et al., 2008; Malaviya

et al., 2009). Hybrid approach of classification for urban LULC has been mapped for

Atlanta metropolitan area is to improve the accuracy of classification from Landsat 7

ETM+ images (Lo and Choi, 2004). Currently, the focus on LULC changes includes the

monitoring and mapping of land use change, the analyzing of driving forces, the


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modeling and predicting of land use change with different scenarios, and the assessing

ecological effects associated with land use change.

In India, several works have been carried out to study the deforestation rates and

changes in LULC (Prakash and Gupta, 1998; Samant and Subramanyam, 1998; Fazal,

2000; Srivastava et al., 2002; Chauhan and Nayak, 2005; Shamsudheen et al., 2005; Jat

et al., 2008; Lele and Joshi, 2009; Malaviya et al., 2009). Interestingly, most of the

works of LULC change has been carried out in biological species rich areas of India

(IIRS, 2002; Prasad et al., 2010). Notably, Assam states of north-eastern region of India

comprising of two mega biodiversity hotspots i.e. Himalayan and Indo-Burma, are

undergoing rapid changes in LULC over the last three decades (Lele and Joshi, 2009).

Keeping these perspectives in view, an attempt was made to provide opportunities to

realize a strategic assessment to determine the LULC change during the past years

(1977-2010), assessment of the impact on forest cover by using integrate field and

image analysis. This study has been undertaken with a hope to meet the challenges in

planning and management especially to control the deforestation of Kamrup district of

Assam.

Materials and Methods

Study area

Kamrup district in Assam state, India extends between 25°46’ to 26°49’ N latitudes

and 90°48’ to 91°50’ E longitudes, covering an area of about 4591.79 km² (Fig. 1). The

Guwahati city is located at the southern bank of mighty river Brahmaputra which is one

of the geometric centers of the study site. Guwahati, one of the most important cities of

the north eastern part of India, located in Kamrup district of Assam which is growing

hastily in size, diversity and population. In Kamrup, rapid urbanization is a result of the

unprecedented population growth. The city of Guwahati in north-eastern region of India

provides a typical case of haphazard and impromptu urbanization. Due to the rapid

growth of the city, the anthropogenic activities have been increasing since the last few

decades. There is a rapid growth of population in the city from 2,92,029 to 27,77,621

persons within a period of 40 years from 1971 to 2011 (Census of India, 1991 and

2011). Secondly, due to the unplanned growth of the city, the LULC has changed day

by day and therefore, a need for proper planning for the careful handling of this

alarming situation is warranted.

The altitude of the study area ranges from 78 to 321 m above mean sea level,

harboring a mosaic of land use types. The geological, geo-morphological and climatic

conditions give rise to younger alluvial soil that mainly Udifluvents (at Brahmaputra

river basin) and Haplastalfs to Rhodustalfs (at the boundary of Meghalaya). The

Alluvial soils are deposited in the site mainly by the flood of the rivers carrying silt and

mostly found in the flood plain tract of the river Brahmaputra (SOE, 2004; IUSS

Working Group WRB, 2014). The textures of the soil are usually sandy, silty, clayey-

loam. The region is characterized by hot sub-humid (moist) to humid (inclusion of per-

humid) climate with alluvium dried soil and growing period of approximately 210+

days. Mean daily temperature ranges from 7.0 to 39.5 °C, and precipitation average

2200 mm year-1

.

The district falls within 9A and 9B biogeographic zones, i.e. north-east Brahmaputra

valley and northeast hills (Rodgers and Panwar, 1988). The region is very rich in floral

and faunal resources, encompasses two protected area of Assam i.e. Deepor Beel and


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Amchang Wildlife Sanctuary. Deepor Beel is located about 10 km south western part of

Guwahati city and it is considered as one of the large and important wetlands in

Brahmaputra valley. This sanctuary has been recognized as a wetland under the Ramsar

Convention, which has listed the lake in November 2002, as a Ramsar site for

undertaking conservation measures on the basis of its rich biological and environmental

importance. It is also categorized of the wetland type under the Burma Monsoon Forest

biogeographic region. Deepor Beel is a natural habitat for approximately 219 species of

birds, including 70 migratory species and some are globally threatened viz., Spotbilled

Pelican (Pelecanus philippensis), Lesser Adjutant Stork (Leptoptilos javanicus) and

Bare’s Pochard (Aythya baeri). Amchang Wildlife Sanctuary located eastern fringe of

the Guwahati city cover 78.64 km² area. Different kinds of mammals, birds and

butterfly are found in this sanctuary. Some species of mammals recorded so far in this

sanctuary are Chinese Pangolin (Manis pentadactyla), Flying Fox (Pteropus giganteus),

Asian Elephant (Elephas maximus), Wild Pig (Sus scrofa), Capped Langur

(Trachypithecus pileatus), Assamese Macaque (Macaca assamensis) and Leopard Cat

(Prionailurus bengalensis). Most of these preferred the habitat of semi-evergreen to

moist-deciduous forest. Land use pattern in the Kamrup district is divided primarily

among tropical semi-evergreen, moist deciduous, dry deciduous, degraded bamboo

forest, sal forest, tea plantation, agriculture and urban land. Unplanned growth of the

Guwahati city, deforestation, timber harvestation, expansion of agricultural land and

encroachment are some major threats for changes the land covers of the Kamrup district

of Assam (India).

Figure 1. Location of the study area Kamrup District of Assam, India.


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Data

This study aims to detect LULC with visual interpretation and supervised

classification, then analyses of changes over the different time periods. The present data

analysis was carried out using six cloud free Landsat remote sensed images viz., two

Multispectral Scanner (MSS) of 1977 covering path and row 029/155 and 029/174 (Fig.

2), two Landsat Thematic Mapper (TM) of 1987 covering path and row of 029/745 and

028/756 (Fig. 2) and two Landsat Enhanced Thematic Mapper plus (ETM+) of 2010

covering path row 218/249 and 218/278 (Fig. 2). The orthorectified Landsat data was

downloaded from GLCF (Global Land Cover Facilities) websites

(http://glcf.umiacs.umd.edu/) at EROS data center, University of Maryland served as

the primary data source to evaluate LULC. The images were downloaded as separate

bands 1-5 and 7 and then stacked in ERDAS Imagine 2010 to give the multispectral

images. Landsat orbits the earth at an altitude of circa 705 km, according to Sun-

synchronous, near-polar orbit with an inclination angle of 98.22 with respect to the

equator. This orbital pattern provides the opportunity to collect imagery at high latitude

regions. The revisit time and hence maximal temporal resolution of the sensor is 16 day.

The ETM+ sensor is an imaging radiometer collecting reflected and emitted energy

from the earth’s surface in eight bands of the electromagnetic spectrum. The ETM+ is

designed to collect, filter and detect radiation from the earth in a swath 185 km wide as

it passes over head and provides the necessary cross-track scanning motion while the

spacecraft orbital motion provides motion an along-track scan. Landsat data contains

different spatial and the spectral wavelength analyzed for the study of LULC changes in

different years (Table 1). Apart from the Landsat satellite data Survey of India (SOI)

topographical maps of 1:50,000 scale was used as a baseline map for the Area of

Interest (AOI), ERDAS Imagine for image processing and analysis and Arc GIS for

further calculation and generation of maps.

Table 1. Spatial and spectral behavior of different series of Landsat satellite sensors.

Resolution Landsat MSS Landsat TM Lamdsat ETM+

Spatial (m) 80 30 30

Spectral (µm)

Band 1 0.50-0.60 (Green) 0.45-0.52 (Blue) 0.45-0.52 (Blue)

Band 2 0.60-0.70 (Red) 0.52-0.60 (Green) 0.53-0.61 (Green)

Band 3 0.70-0.80 (Blue) 0.63-0.69 (Red) 0.63-0.69 (Red)

Band 4 0.80-1.10 (IR) 0.79-0.90 (NIR) 0.75-0.90 (NIR)

Band 5 1.55-1.75 (SWIR) 1.55-1.75 (SWIR)

Band 6 10.4-12.5 (TIR) 10.40-12.50 (TIR)

Band 7 2.08-2.35 (SWIR) 2.1-2.35 (SWIR)

Band 8 0.52-0.90 (PAN)

IR: Infrared, NIR: Near Infrared, SWIR: Short Wavelength Infrared, TIR: (Thermal Infrared), SWIR:

Short Wavelength Infrared, PAN: Panchromatic.


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Figure 2. False color composites (FCC) for the study area between 1977 to 2010 (Landsat

MSS, February 1977; Landsat TM, November 1987; Landsat ETM+, February 2010.) showing

difference in forest cover, and also the variation in color tone and texture.

Different Images of Landsat data pre-processed, in this study image enhancement

and georeferencing has been done. Georeferencing performed by registered Landsat

images geometrically using topographical map of Survey of India (SOI) on 1:50,000

scale in ERDAS Imagine. The common uniformly distributed GCPs (Ground Control

Points) were marked with root mean square of one pixel and the image was re-sampled

at nearest neighbor method. After that the study area (AOI) extracted from georefrence

images of different year by overlaying the boundary data provided by SOI

topographical maps.

Vegetation indices

Normalized Difference Vegetation Index (NDVI) is a ratio that uses the near infrared

and red bands to distinguish the differences between vegetated and non vegetated area

or it measures the abundance and growth condition of vegetation. The value of NDVI

ranges between -1 to +1 means, higher the value of NDVI means vegetated area and

lower value non-vegetated area. The development of vegetation indices from satellite

images facilitated the process of differentiating and mapping vegetation by providing

valuable information about structure and composition. In tropical forests, the NDVI

from Landsat has demonstrated to be an indicator of overall canopy structure,

vegetation cover, tree density and species diversity (Oza et al., 1996; Sanchez-Azofeia

et al., 2003; Krishnaswamy et al., 2004; Feeley et al., 2005). The NDVI of different

time period of Landsat images of Kamrup district was calculated following Equations 1

and 2, using ERDAS Imagine 2010 (Fig. 3).

Figure 3. NDVI map for the Kamrup district between 1977 to 2010 showing changes in NDVI

value with increases the area of contrast color.


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NDVI (MSS) = NIR (Band 4)-Red (Band 2)/NIR (Band 4) +Red (Band 2) (Eq. 1)

NDVI (TM and ETM+) = (Band 4)-Red (Band 3)/NIR (Band 4) +Red (Band 3) (Eq. 2)

Species composition under different land cover

In the present study, major forest cover types were classified into two categories viz.,

dense forest and open forest on the basis of percentage of canopy cover. Forests with

>40-80% and >20-40% canopy cover were classified in the dense forest and open forest

respectively. Further dense and open forests were analyzed on the basis of species

composition.

Land use and land cover classification

Supervised classification method was used for mapping of LULC of the study area.

This has been frequently used in image classification. In supervised classification, the

area of known identity was used to classify pixels of unknown area and training sites is

closely controlled by analyst. The spectral behavior of training sites gives information

of the classes of land cover such as dense forest, open forest, degraded forest,

agriculture pasture, scrub and water bodies in the images. The selection of training sites

and classification of different classes of land cover of study area were done using

ERDAS Imagine software under the process of maximum likelihood classifier for the

recent remote sensed data (Landsat ETM+).

Accuracy assessment

Accuracy assessment for the LULC maps of the Kamrup district was based on the

ground truth points recorded during field survey. These points were collected in

stratified random manner using a GPS. Error matrices were used to calculate user’s and

producer’s accuracies for the forests cover class, over all accuracies and Kappa

Coefficient for each image individually as well as collectively.

Change detection

To quantitatively describe the LULC change, a Net Change Ratio (NCR) is used to

enable the compression between the extents of LULC change between two land covers

(Dong et al., 2010):

NCR = (Aie – Ais)/Ais × 100% (Eq. 3)

where Ais is the area of ith

land cover type in the first year and Aie is the area of ith

land

cover type in the last year.

In addition, to analysing and understanding the characteristics patterns in a region,

many landscape matrices were computed. The Shannon-Weaver diversity index (H) is

widely used in landscape ecology. It is defined as follows (Derry et al., 1998):

H= -∑ pi × ln (pi) (Eq. 4)

where pi is the ratio of land use and land cover type i with respect to that to the total

area and m is the total number of land cover types.


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H is a unit-less measure that increases with increasing heterogeneity of the sample unit

of the landscape. This index is based on determining the uncertainty in a randomly

selected land cover types of the landscape.

Analysis of the rate of land use change

Two kinds of land use change rate were focused on in this case study, i.e. the

changes rate of single land use, and the integrated land use change for the whole region

(Zhu et al., 2001).

Change rate of single land use as a dynamic degree can be quantitatively measure the

change of a certain land use type. This index is recognized as one of the most widely

used indices for detecting the land use change rate. It is mostly calculated according to

equation (5):

K= (Ub-Ua)/Ua × 1/T ×100 (Eq. 5)

where K is land use dynamic degree, measuring the change rate of the target land use

type; Ua and Ub are the area of the target land use type at the beginning and end of the

study period respectively; and T is the study period, which is usually measured with the

unit of year.

Results

Land use and land cover

Six major LULC categories were delineated using Landsat data and field

investigation viz., dense forest, open forest, agriculture land, settlement, water bodies

and sand (Fig. 4). In the year of 1977, dense forest cover was accounted about 29.08%

(1335.42 km²) that has been decreased on 23.83% (1094.16 km²) in 1987 and only

22.34% (1025.80 km²) in 2010 respectively in the entire study area of 4,591.79 km²

(Table 2 and 3). Dominated tree species composition of different LULC is summarized

in Table 4. The total dense forests of the Kamrup district was degraded (6.47%) within

last three decades (1977-2010), in the form of selective logging, fire, grazing, fuel and

timber wood collection, leading to growth of secondary successional stage. The study

also emphasis that, the dense forest cover area (309.62 km²) is transformed in to another

category of land cover, indicated that high anthropogenic pressure in the district. The

result (Fig. 4 and Fig. 5) also suggested that the surface area of dense forest, agriculture

land and water bodies were decreased as a result of LULC changed.

Land use change

The land uses have changed significantly during the period of 1977 to 1987 in

Kamrup district of Assam (Table 2 and 3). Agriculture land decreased more than as

much as dense forest and water bodies classes; whereas urban settlement, sand and open

forest cover classes areas increased during 1977 to 1987. From 1987 to 2010 the

changes in LULC classes is much lower than the previous changes and during this

agriculture, dense forest and sand cover class area was decreased.


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Figure 4. Class map of Vegetation and land cover of the study area in 1977 to 2010.

Figure 5. Changes in LULC (1977 to 2010) in different land use (1: Dense forest, 2: Open

forest, 3: Agriculture land, 4: Urban settlement, 5: Sand, 6: Water bodies).

Table 2. Land use and land cover change of Kamrup, Assam, India.

Class Dense

Forest

Open

Forest

Agriculture

land Settlement Sand

Water

bodies Year

1977 (km²) 1335.42 677.21 1479.81 559.41 101.90 438.04

1987 (km²) 1094.16 891.91 1107.88 986.43 162.71 348.69

2010 (km²) 1025.80 920.98 995.72 1128.30 148.18 372.84

Net Change Ratio

(%) 1977/1987 -18.07 31.70 -25.10 76.33 59.68 -20.40

Net Change Ratio

(%) 1987/2010 -6.20 3.26 -10.00 14.40 -8.90 6.93

Total Net Change Ratio

(%) 1977/2010 -23.20 36.00 -32.70 101.70 45.41 -14.90


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Table 3. Percentage area covered in different land use categories and SHDI during 1977-

2010.

Year Area covered (%)

SHDI Dense

Forest

Open

Forest

Agriculture

Land

Urban Settlement Sand Water

bodies

1977 29.08 14.75 32.23 12.18 2.22 9.54 1.571

1987 23.83 19.42 24.13 21.48 3.54 8.37 1.640

2010 22.34 20.06 21.68 24.57 3.23 8.11 1.658

Table 4. Species composition in different land use categories.

Sl.

No.

Classification and species composition in different land use and land cover classes

1. Dense Forest

1.1 Semi-evergreen Forest

Alstonia scholaris, Antidesma ghaesembilla, Artocarpus chama, Bauhinia purpurea, Bridelia

stipularis, Dillenia indica, Duabanga grandiflora, Engelhardtia spicata, Gmelina arborea,

Lagerstroemia parviflora, Litsea salicifolia, Mallotus philippensis, Schima wallichii, Protium

serratum, Frmiana simplex, Syzygium cumini, Stereospermum tetragonum, Terminalia chebula,

Magnolia champaca, Toona ciliata

1.2 Moist-deciduous Forest

1.2.1 Sal Forest

Dominated by Shorea robusta, Schima wallichii

1.2.2 Wet Hill Sal Forest

Shorea robusta is usually associated with Lagerstroemia parviflora, Lagerstroemia speciosa,

Schima wallichii, Stereospermum tetragonum, besides these species other common tree species

of this forest are; Artocarpus chama, Bauhinia variegata, Bischofia javanica, Bridelia retusa,

Careya arborea, Callicarpa arborea, Derris indica, Dillenia pentagyna, Gmelina arborea,

Balakata baccata, Erminalia bellirica, Mallotus philippensis, Vitex peduncularis, Ziziphus

jujuba

1.2.3 Moist Sal Forest

Artocarpus chama, Careya arborea, Derris indica, Dillenia pentagyna, Rhus sp., Schima

wallichii, Ficus hispida, Kydia calycina, Balakata baccata, Holarrhena pubescens, Wrightia

arborea.

2. Open Forest

2.1 Dry-deciduous Forest

Aegle marmelos, Albizia procera, Bombax ceiba, Callicarpa arborea, Cassia fistula,

Dalbergia sp., Dalbergia pinnata, Gmelina arborea, Kydia calycina, Litsea monopetala,

Mallotus philippensis, Melia azedarach, Oroxylum indicum, Shorea robusta, Catunaregam

spinosa, Phyllanthus emblica, Tectona grandis, Wrightia arborea, Streblus asper, Semecarpus

anacardium, Ziziphus jujuba.

2.2 Degraded and Shrub land

Bombax cebia, Cassia fistula, Ficus hispida, Phyllanthus emblica, Spondias pinnata, Streblus

asper, Oroxylum indicum.

3. Agriculture land

Mostly rice dominated agricultural land with side by side several tree sps. and bamboo are such

as Trewia nudiflora, Bombax ceiba, Cassia fistula, Termenallia bellirica, Ficus hispida, Toona

ciliata, Dendrocalamus hamiltonii, Bambusa tulda, Bambusa balcooa.

4. Water bodies/ Wetlands

The wetland are mostly dominated by several hydrophytes species such as Eichhornia

crassipes, Lemna perpusilla, Pistia stratiotes, Salvinia aucullata, Ceratophyllum demersum,

Utricularia aurea, Nymphea nouchali, Ipomea aquatica, Limnophila sp., Polygonum glabrum


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Change detection and landscape indices

The Shannon-Weaver Diversity index (SHDI) of landscape increase significantly

during the year of 1977-1987. Higher the value of SHDI in a landscape refers to more

landscape elements. The value of the SHDI during the year 1987-2010 was little low

with some increase from 1.640 to 1.658 (Table 3).

Accuracy assessment

The accuracy assessment is conceded out to evaluate the quality of thematic maps

revealed from remote sensed data in a consequential way. The overall accuracy of

classification was 76.34% for the 1977 MSS, 89.56% for the 1987 TM image, and

92.34% for the 2010 ETM+ images and the Kappa Coefficients was 0.84, 0.89 and 0.92

respectively (Table 5).

Table 5. Accuracy assessment evaluation in different classify images of different time scale.

Sl. No. Classes

1977 1987 2010

UA* PA** UA* PA** UA* PA**

1 Dense forest 69.5 70.2 72.5 92.5 87.3 94.6

2 Open forest 71.5 83.5 85.5 96.3 89.5 96.4

3 Agriculture land 81.3 94.6 91.0 93.4 92.5 100

4 Urban settlement 70.5 92.5 92.5 97.8 94.0 98.9

5 Sand 70.2 90.5 90.5 96.3 91.0 96.4

6 Water bodies 96 100 100 100 100 100

7 Overall accuracy 76.34 89.56 92.34

8 Kappa statistics 0.84 0.89 0.92

*UA: User’s accuracy (%); **PA: Producer’s accuracy (%).

Discussion

Satellite remote sensed dataset facilitates monitoring of LULC implementation. In

Kamrup district of Assam province of north-eastern region of India, Landsat images

have been used for study the land cover changes during last few decades. In India, the

forests are being disturbed from pre-independence period until today for various

rationales (Stebbing, 1992; Bhat et al., 2000; Jayakumar et al., 2009). Zeng et al. (2000)

reported the usefulness of NDVI to assess the percentage of vegetation cover and

opined that higher the NDVI value indicates the rich vegetation density. This technique

has also being assisted with Landsat ETM+ data vis-à-vis analysis of other satellite

images for digital mapping of forest density (Kumar et al., 2007), soil mapping and

different LULC (Boettinger et al., 2008). Primary feature of vegetation viz., light, water

and soil conditions are also being affected due to additional deforestation and clear-cut

timber harvestation (Kumar et al., 2007; Boettinger et al., 2008). The present study

showed positive discrimination of gradual increment of NDVI value, indicates that

anthropogenic pressure increase in time dependent manner, which is reflected at our


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investigation during last few decades (1977 to 2010) in Kamrup district of Assam (Fig.

3).

A significant declination of about 56 % (> 2.9 million ha) in lowland protected forest

of Kalimantan’s has been assessed and monitored by Landsat temporal data (Curran et

al., 2004). By using a similar approach, Srivastava et al. (2002) reported increased

insurgency problem and constant increase in the anthropogenic pressure in Sonitpur

district of Assam, resulted maximum loss of forest cover about 229.64 km². Present

investigation showed massive reduction in forest and agriculture land in time dependent

manner importantly in different LULC classes from 1977 to 2010 and has positive

correlation with study of Ardi and Wolff, (2009) who reported the significant loss of

woodland area in Bindura district of Zimbabwe 17% (380.1 km2) and 11% (304.8 km

2)

during 1973-1989 and 1989-2000 respectively using the same approach.

Various dilemmas are also persists for image enhancement procedures and its

inability to differentiate accurate variation of soil moisture and vegetation phenology

moved toward land cover changes. The use of classification techniques avoided this

problem. When carried out independent supervised classification, classes which present

very different spectral signatures at different dates can be classified in to the same land

cover. In the present study, spectral classes corresponded to different classes of changes,

and multi-date unsupervised classification did not allow the accurate identification of

the land cover changes. Results suggest that the principal land cover changes in the

Kamrup (Assam, India) such as, deforestation, timber harvestation and conversion of

agriculture land to buildup area can be monitored accurately by temporally remote

sensed images (Fig. 4).

Comprehensive analysis revealed that in the year 1977, agriculture land cover was

32.23% of the total study area, which can be considered as the majority of the land

cover. The land cover was remained a major part despite the diminishment of land cover

as 24.13% to 21.68% from the year 1987-2010 (Table 3). Same pattern of declination

was observed in land cover area constituting dense forest and water bodies from 1977 to

1987, with a net change ratio of -18.08%, and -20.40% respectively. In contrast, open

forest, urban settlement and sand land surface area increases gradually over 1977 to

1987, with a net change ratio of 31.70%, 76.33% and 59.68% respectively (Table 2).

The annual rate of land use change (K) was measured as highest for urban settelement

class (7.63%), whereas dense forest, agriculture land and water bodies did reveal the

lowest values i.e. -18.0%, -25.1% and -20.3% respectively during the 1977 to 1987

(Fig. 6). An inclusive comparison of land use area revealed the loss of dense forest,

agriculture land and sand area during 1987-2010 with the net change ratio of -6.20%, -

10.0% and -8.90% and the rate of land use change -0.32%, -0.53% and -0.46%

respectively. These data are in favor of the study of Yu et al. (2007), who reported the

overall open forests, grassland and river-bed area loss as 69.6 ha (3.4 %), 16.6 ha (2%)

and 20.9 ha (15%) between 1976-2005 in Briahi Ganga Sub-watershed of Garhwal

Himalaya, Uttrakhand using Landsat MSS, TM and IRS 1D LISS III images. This

change detection techniques may be described as a reason of increasing anthropogenic

activity such as mining (Srivastava et al., 2002), deforestation, urbanization, causes

increased diversity indices (SHDI) during 1977 to 2010 with increased in the

fragmentation of forest, scrub and agriculture land cover in to different new land use

classes such as mine-water and waste land.


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Figure 6. Change rate of single land use type in study area during 1977 to 1987 and 1987-2010

(1: Dense forest, 2: Open forest, 3: Agriculture land, 4: Urban settle, 5: Sand, 6: Water bodies).

Another important factor which gets consideration is the policy of Indian government

after independence which enables the migration of people from neighboring countries

without any predicament and subsequently facilitate inhabit in this particular region.

The fact gets support from data of drastic increase in population of Assam province and

Kamrup district in particular with 53.26 and 65.72 respectively during 1971/1999

(Table 6) and population statistics (SOE, 2004) of this district from 1901 to 2011

showed continuous increase (Fig. 7). This rapid immigration in Kamrup district resulted

in the rapid loss of dense forest area to cope up with the demand for basic needs of the

expanding population. Initially people cleared the forest for settlement and other

purpose, and in recently started expanding the area for agriculture purpose too. Overall

the settlement and open forest areas increased 568.89 km2 and 243.77 km

2 from 1977

to 2010, with corresponding decrease in dense forest and agriculture area 309.62 km2

and 484.09 km2 respectively. The 2011 year census of this district reported a hike

density of population from 460 (person per km2) in 1991 to 604 in 2011. The rise in

population density led to higher consumption of resources per capita and the increased

need for built environment (Rajesh and Prasad, 2005; Prasad et al., 2010).

Table 6. Comparison of Decadal percentage variation of population in different year

(Source: Census of India, 2011)

Decadal Percentage Variation of Population in different year

1901/11 1911/21 1921/31 1931/41 1941/51 1951/61 1961/71 1971/99 1999/2001 2001/11

Assam 16.99 20.48 19.91 20.40 19.93 34.98 34.95 53.26 18.92 16.93

Kamrup 11.10 7.06 9.38 19.21 17.17 37.73 38.80 65.72 26.11 17.13


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Figure 7. Population increase in Assam, India (1901–2011).

Figure 8. Photos showing disturbance in natural landscapes, a) clear cut timber harvestation

for stone mining and b) expansion of Guwahati Metropolitan city towards the natural hills

forest of Kamrup.

Our study ascertained that maximum loss (484.09 km²) was recorded in the case of

agriculture land cover and decline rate was more pronounced between 1977-1987

(371.93 km²) than 1987-2010 (112.16 km²). An area of 309.62 km² covered under dense

forest was subsequently lost meanwhile gradual increment of urban settlement and open

forest area as 568.86 km² and 243.77 km² respectively. The significance increase of

urban settlements, open forest and sand land area with NCR were 101.70, 36.00 and

45.40 % during the period of 1977 to 2010, respectively (Table 2). The present analysis

concluded that the high intensity of anthropogenic pressure in Kamrup district, (Assam)

and in turn subsequently changing land cover had changed (Fig. 8). Present study also

confirmed the LULC change during 1977-2010, explains degradation within the

protected areas such as Amchang Wildlife Sanctuary. The proliferation of human

settlements, roads and industry around the periphery (eastern and north-eastern sides)

causing the pollution problems in Deepor Beel (Ramsar site), which are the natural


- 235 -





inhabitant of many important birds species and Asian elephants. A present trend of

deforestation and anthropogenic activities has been reached at alarming condition and

there by leading to the losses of forest cover. Near issues suggests the urgent need of

monitoring the change of vegetation, land use and current status of all forest in

protected areas of India to met the challenge for effective management and implication

of refined conservation strategies.

Conclusions

The present study highlights the changes in LULC, based on the Landsat temporal

remote sensed images of the year 1977, 1987 and 2010, using remote sensing and GIS

techniques. During the span of 34 years, dense forest and agriculture land acreage

decreased notably. Open forest and urban settlement land acreage however increased

significantly. The changes in area of dense forest in different LULC categories viz.,

open forest and settlement in a form of lesser vegetation caused degradation of natural

forests. The Shannon-Weaver diversity index and the extent of landscape heterogeneity

have changed significantly. Anthropogenic impact factors, such as excessive

deforestation, encroachment, human settlement, grazing, timber harvestation and

overexploitation of natural resources accelerated the deterioration of local environment

in the Kamrup district of Assam, India. Therefore, a perspective measure has to be taken

and future research work is required to avoid the loss of forest area in future.

Acknowledgements. The facilities provided by the Director, G.B. Pant National Institute of Himalayan

Environment and Sustainable Development, Almora, Uttarakhand are gratefully acknowledged. The

author would like to also thank the Assam Forest Department, Government of Assam, Guwahati for

providing permission to access the site.

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